Mobilization, self-expression or argument? A computational method for identifying language styles in political discussion on Twitter
ISSN: 1468-4527
Article publication date: 22 January 2024
Issue publication date: 11 July 2024
Abstract
Purpose
This study develops a computational method to investigate the predominant language styles in political discussions on Twitter and their connections with users' online characteristics.
Design/methodology/approach
This study gathers a large Twitter dataset comprising political discussions across various topics from general users. It utilizes an unsupervised machine learning algorithm with pre-defined language features to detect language styles in political discussions on Twitter. Furthermore, it employs a multinomial model to explore the relationships between language styles and users' online characteristics.
Findings
Through the analysis of over 700,000 political tweets, this study identifies six language styles: mobilizing, self-expressive, argumentative, narrative, analytic and informational. Furthermore, by investigating the covariation between language styles and users' online characteristics, such as social connections, expressive desires and gender, this study reveals a preference for an informational style and an aversion to an argumentative style in political discussions. It also uncovers gender differences in language styles, with women being more likely to belong to the mobilizing group but less likely to belong to the analytic and informational groups.
Practical implications
This study provides insights into the psychological mechanisms and social statuses of users who adopt particular language styles. It assists political communicators in understanding their audience and tailoring their language to suit specific contexts and communication objectives.
Social implications
This study reveals gender differences in language styles, suggesting that women may have a heightened desire for social support in political discussions. It highlights that traditional gender disparities in politics might persist in online public spaces.
Originality/value
This study develops a computational methodology by combining cluster analysis with pre-defined linguistic features to categorize language styles. This approach integrates statistical algorithms with communication and linguistic theories, providing researchers with an unsupervised method for analyzing textual data. It focuses on detecting language styles rather than topics or themes in the text, complementing widely used text classification methods such as topic modeling. Additionally, this study explores the associations between language styles and the online characteristics of social media users in a political context.
Keywords
Acknowledgements
The author would like to thank Dr Douglas Steinley for his suggestions for constructing cluster models. This paper was initially developed in his Cluster Analysis class. The author would also like to express his gratitude to his friend Yoonjae Shin and the Writing Center at the University of Missouri, Columbia. They provided numerous insights into improving the writing of this paper. Additionally, the author appreciates the work of the editor and anonymous reviewers of Online Information Review. Their invaluable feedback significantly enhanced this paper.
Citation
Hu, L. (2024), "Mobilization, self-expression or argument? A computational method for identifying language styles in political discussion on Twitter", Online Information Review, Vol. 48 No. 4, pp. 783-802. https://doi.org/10.1108/OIR-10-2022-0545
Publisher
:Emerald Publishing Limited
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